Overview

Dataset statistics

Number of variables17
Number of observations191
Missing cells17
Missing cells (%)0.5%
Duplicate rows2
Duplicate rows (%)1.0%
Total size in memory25.5 KiB
Average record size in memory136.7 B

Variable types

Text1
Numeric15
Categorical1

Alerts

Dataset has 2 (1.0%) duplicate rowsDuplicates
Anger is highly overall correlated with Causing Disrepute and 10 other fieldsHigh correlation
Causing Disrepute is highly overall correlated with Anger and 10 other fieldsHigh correlation
Developing own business is highly overall correlated with Anger and 10 other fieldsHigh correlation
Disrupt Public Service is highly overall correlated with Anger and 9 other fieldsHigh correlation
Extortion is highly overall correlated with Anger and 10 other fieldsHigh correlation
Fraud is highly overall correlated with Anger and 10 other fieldsHigh correlation
Others is highly overall correlated with Anger and 9 other fieldsHigh correlation
Personal Revenge is highly overall correlated with Anger and 10 other fieldsHigh correlation
Prank is highly overall correlated with Anger and 10 other fieldsHigh correlation
Sale purchase illegal drugs is highly overall correlated with Steal InformationHigh correlation
Sexual Exploitation is highly overall correlated with Anger and 10 other fieldsHigh correlation
Spreading Piracy is highly overall correlated with Anger and 10 other fieldsHigh correlation
Steal Information is highly overall correlated with Sale purchase illegal drugsHigh correlation
Total is highly overall correlated with Anger and 10 other fieldsHigh correlation
Abetment to Suicide is highly imbalanced (85.8%)Imbalance
Personal Revenge has 66 (34.6%) zerosZeros
Anger has 75 (39.3%) zerosZeros
Fraud has 34 (17.8%) zerosZeros
Extortion has 65 (34.0%) zerosZeros
Causing Disrepute has 74 (38.7%) zerosZeros
Prank has 106 (55.5%) zerosZeros
Sexual Exploitation has 50 (26.2%) zerosZeros
Disrupt Public Service has 134 (70.2%) zerosZeros
Sale purchase illegal drugs has 170 (89.0%) zerosZeros
Developing own business has 123 (64.4%) zerosZeros
Spreading Piracy has 133 (69.6%) zerosZeros
Psycho or Pervert has 175 (91.6%) zerosZeros
Steal Information has 158 (82.7%) zerosZeros
Others has 44 (23.0%) zerosZeros
Total has 9 (4.7%) zerosZeros

Reproduction

Analysis started2024-02-21 16:33:03.481116
Analysis finished2024-02-21 16:33:24.849416
Duration21.37 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

City
Text

Distinct80
Distinct (%)42.1%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
2024-02-21T22:03:24.983481image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Length

Max length28
Median length16
Mean length9.5736842
Min length3

Characters and Unicode

Total characters1819
Distinct characters50
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)21.1%

Sample

1st rowAgra
2nd rowAllahabad
3rd rowAmritsar
4th rowAsansol
5th rowAurangabad
ValueCountFrequency (%)
pradesh 20
 
7.4%
12
 
4.4%
total 12
 
4.4%
chandigarh 5
 
1.9%
gujarat 4
 
1.5%
uttarakhand 4
 
1.5%
tripura 4
 
1.5%
uttar 4
 
1.5%
lakshadweep 4
 
1.5%
delhi 4
 
1.5%
Other values (79) 197
73.0%
2024-02-21T22:03:25.300484image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 353
19.4%
r 133
 
7.3%
h 125
 
6.9%
i 86
 
4.7%
n 82
 
4.5%
d 82
 
4.5%
80
 
4.4%
s 80
 
4.4%
l 71
 
3.9%
t 66
 
3.6%
Other values (40) 661
36.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1416
77.8%
Uppercase Letter 290
 
15.9%
Space Separator 80
 
4.4%
Other Punctuation 16
 
0.9%
Open Punctuation 8
 
0.4%
Close Punctuation 8
 
0.4%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 353
24.9%
r 133
 
9.4%
h 125
 
8.8%
i 86
 
6.1%
n 82
 
5.8%
d 82
 
5.8%
s 80
 
5.6%
l 71
 
5.0%
t 66
 
4.7%
e 66
 
4.7%
Other values (13) 272
19.2%
Uppercase Letter
ValueCountFrequency (%)
T 36
12.4%
A 30
 
10.3%
P 28
 
9.7%
M 23
 
7.9%
D 19
 
6.6%
N 18
 
6.2%
K 15
 
5.2%
U 12
 
4.1%
H 12
 
4.1%
L 11
 
3.8%
Other values (12) 86
29.7%
Space Separator
ValueCountFrequency (%)
80
100.0%
Other Punctuation
ValueCountFrequency (%)
& 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1706
93.8%
Common 113
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 353
20.7%
r 133
 
7.8%
h 125
 
7.3%
i 86
 
5.0%
n 82
 
4.8%
d 82
 
4.8%
s 80
 
4.7%
l 71
 
4.2%
t 66
 
3.9%
e 66
 
3.9%
Other values (35) 562
32.9%
Common
ValueCountFrequency (%)
80
70.8%
& 16
 
14.2%
( 8
 
7.1%
) 8
 
7.1%
- 1
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1819
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 353
19.4%
r 133
 
7.3%
h 125
 
6.9%
i 86
 
4.7%
n 82
 
4.5%
d 82
 
4.5%
80
 
4.4%
s 80
 
4.4%
l 71
 
3.9%
t 66
 
3.6%
Other values (40) 661
36.3%

Personal Revenge
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct54
Distinct (%)28.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean65.084211
Minimum0
Maximum1470
Zeros66
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:25.473888image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q316
95-th percentile440.7
Maximum1470
Range1470
Interquartile range (IQR)16

Descriptive statistics

Standard deviation223.85481
Coefficient of variation (CV)3.4394642
Kurtosis22.993812
Mean65.084211
Median Absolute Deviation (MAD)4
Skewness4.6975298
Sum12366
Variance50110.977
MonotonicityNot monotonic
2024-02-21T22:03:25.625984image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 66
34.6%
2 14
 
7.3%
4 9
 
4.7%
1 8
 
4.2%
5 7
 
3.7%
7 7
 
3.7%
8 5
 
2.6%
11 5
 
2.6%
6 5
 
2.6%
14 4
 
2.1%
Other values (44) 60
31.4%
ValueCountFrequency (%)
0 66
34.6%
1 8
 
4.2%
2 14
 
7.3%
3 3
 
1.6%
4 9
 
4.7%
5 7
 
3.7%
6 5
 
2.6%
7 7
 
3.7%
8 5
 
2.6%
9 3
 
1.6%
ValueCountFrequency (%)
1470 1
0.5%
1463 1
0.5%
1207 1
0.5%
1195 1
0.5%
794 1
0.5%
783 1
0.5%
654 1
0.5%
628 1
0.5%
620 1
0.5%
555 1
0.5%

Anger
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)27.4%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean40.926316
Minimum0
Maximum822
Zeros75
Zeros (%)39.3%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:25.774504image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310.75
95-th percentile209.1
Maximum822
Range822
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation134.83521
Coefficient of variation (CV)3.2945846
Kurtosis19.655921
Mean40.926316
Median Absolute Deviation (MAD)2
Skewness4.4186861
Sum7776
Variance18180.534
MonotonicityNot monotonic
2024-02-21T22:03:25.914401image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 75
39.3%
1 15
 
7.9%
2 11
 
5.8%
3 9
 
4.7%
4 9
 
4.7%
8 5
 
2.6%
10 4
 
2.1%
5 4
 
2.1%
6 4
 
2.1%
9 3
 
1.6%
Other values (42) 51
26.7%
ValueCountFrequency (%)
0 75
39.3%
1 15
 
7.9%
2 11
 
5.8%
3 9
 
4.7%
4 9
 
4.7%
5 4
 
2.1%
6 4
 
2.1%
7 3
 
1.6%
8 5
 
2.6%
9 3
 
1.6%
ValueCountFrequency (%)
822 1
0.5%
814 1
0.5%
714 1
0.5%
712 1
0.5%
581 2
1.0%
461 1
0.5%
457 1
0.5%
263 1
0.5%
210 1
0.5%
208 1
0.5%

Fraud
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct117
Distinct (%)61.6%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1337.9421
Minimum0
Maximum30142
Zeros34
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:26.053417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.25
median44
Q3346
95-th percentile7772.45
Maximum30142
Range30142
Interquartile range (IQR)341.75

Descriptive statistics

Standard deviation4629.6585
Coefficient of variation (CV)3.4602831
Kurtosis24.70273
Mean1337.9421
Median Absolute Deviation (MAD)44
Skewness4.8525513
Sum254209
Variance21433738
MonotonicityNot monotonic
2024-02-21T22:03:26.198422image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 34
 
17.8%
3 7
 
3.7%
19 4
 
2.1%
7 4
 
2.1%
23 3
 
1.6%
15 3
 
1.6%
8 3
 
1.6%
47 3
 
1.6%
11 3
 
1.6%
6 3
 
1.6%
Other values (107) 123
64.4%
ValueCountFrequency (%)
0 34
17.8%
1 2
 
1.0%
2 3
 
1.6%
3 7
 
3.7%
4 2
 
1.0%
5 2
 
1.0%
6 3
 
1.6%
7 4
 
2.1%
8 3
 
1.6%
9 2
 
1.0%
ValueCountFrequency (%)
30142 1
0.5%
30075 1
0.5%
26891 1
0.5%
26853 1
0.5%
15051 1
0.5%
14992 1
0.5%
12213 1
0.5%
12139 1
0.5%
11381 1
0.5%
9680 1
0.5%

Extortion
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct64
Distinct (%)33.7%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean98.731579
Minimum0
Maximum2440
Zeros65
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:26.344554image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q325
95-th percentile532.3
Maximum2440
Range2440
Interquartile range (IQR)25

Descriptive statistics

Standard deviation347.41584
Coefficient of variation (CV)3.5187915
Kurtosis26.985353
Mean98.731579
Median Absolute Deviation (MAD)6
Skewness5.015755
Sum18759
Variance120697.76
MonotonicityNot monotonic
2024-02-21T22:03:26.491278image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 65
34.0%
1 11
 
5.8%
7 8
 
4.2%
11 6
 
3.1%
3 6
 
3.1%
4 6
 
3.1%
15 5
 
2.6%
21 4
 
2.1%
6 4
 
2.1%
17 4
 
2.1%
Other values (54) 71
37.2%
ValueCountFrequency (%)
0 65
34.0%
1 11
 
5.8%
2 3
 
1.6%
3 6
 
3.1%
4 6
 
3.1%
5 3
 
1.6%
6 4
 
2.1%
7 8
 
4.2%
8 1
 
0.5%
9 2
 
1.0%
ValueCountFrequency (%)
2440 1
0.5%
2411 1
0.5%
1842 1
0.5%
1827 1
0.5%
1055 1
0.5%
1050 1
0.5%
1025 1
0.5%
906 1
0.5%
891 1
0.5%
544 1
0.5%

Causing Disrepute
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)32.6%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean92.210526
Minimum0
Maximum1874
Zeros74
Zeros (%)38.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:26.630482image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q325
95-th percentile466.45
Maximum1874
Range1874
Interquartile range (IQR)25

Descriptive statistics

Standard deviation308.81452
Coefficient of variation (CV)3.349016
Kurtosis19.978966
Mean92.210526
Median Absolute Deviation (MAD)3
Skewness4.444971
Sum17520
Variance95366.41
MonotonicityNot monotonic
2024-02-21T22:03:26.777939image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
38.7%
2 9
 
4.7%
1 9
 
4.7%
3 8
 
4.2%
4 5
 
2.6%
9 4
 
2.1%
5 4
 
2.1%
6 4
 
2.1%
19 3
 
1.6%
15 3
 
1.6%
Other values (52) 67
35.1%
ValueCountFrequency (%)
0 74
38.7%
1 9
 
4.7%
2 9
 
4.7%
3 8
 
4.2%
4 5
 
2.6%
5 4
 
2.1%
6 4
 
2.1%
7 3
 
1.6%
8 3
 
1.6%
9 4
 
2.1%
ValueCountFrequency (%)
1874 2
1.0%
1706 1
0.5%
1678 1
0.5%
1212 1
0.5%
1205 1
0.5%
1002 1
0.5%
997 1
0.5%
953 1
0.5%
547 1
0.5%
368 1
0.5%

Prank
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)17.9%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean35.847368
Minimum0
Maximum1385
Zeros106
Zeros (%)55.5%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:26.907601image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile191.55
Maximum1385
Range1385
Interquartile range (IQR)4

Descriptive statistics

Standard deviation175.97695
Coefficient of variation (CV)4.9090619
Kurtosis49.013084
Mean35.847368
Median Absolute Deviation (MAD)0
Skewness6.8611815
Sum6811
Variance30967.887
MonotonicityNot monotonic
2024-02-21T22:03:27.035215image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 106
55.5%
3 12
 
6.3%
1 12
 
6.3%
2 10
 
5.2%
7 5
 
2.6%
6 5
 
2.6%
4 4
 
2.1%
5 4
 
2.1%
10 3
 
1.6%
18 2
 
1.0%
Other values (24) 27
 
14.1%
ValueCountFrequency (%)
0 106
55.5%
1 12
 
6.3%
2 10
 
5.2%
3 12
 
6.3%
4 4
 
2.1%
5 4
 
2.1%
6 5
 
2.6%
7 5
 
2.6%
8 2
 
1.0%
10 3
 
1.6%
ValueCountFrequency (%)
1385 1
0.5%
1384 1
0.5%
1293 1
0.5%
321 1
0.5%
314 1
0.5%
296 1
0.5%
288 1
0.5%
254 1
0.5%
252 1
0.5%
192 1
0.5%

Sexual Exploitation
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct83
Distinct (%)43.7%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean143.64211
Minimum0
Maximum3293
Zeros50
Zeros (%)26.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:27.174892image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median13
Q357.75
95-th percentile588.6
Maximum3293
Range3293
Interquartile range (IQR)57.75

Descriptive statistics

Standard deviation475.2167
Coefficient of variation (CV)3.3083384
Kurtosis24.923
Mean143.64211
Median Absolute Deviation (MAD)13
Skewness4.8594869
Sum27292
Variance225830.91
MonotonicityNot monotonic
2024-02-21T22:03:27.326083image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 50
26.2%
1 8
 
4.2%
3 7
 
3.7%
2 6
 
3.1%
10 4
 
2.1%
4 3
 
1.6%
65 3
 
1.6%
7 3
 
1.6%
85 3
 
1.6%
16 3
 
1.6%
Other values (73) 100
52.4%
ValueCountFrequency (%)
0 50
26.2%
1 8
 
4.2%
2 6
 
3.1%
3 7
 
3.7%
4 3
 
1.6%
5 3
 
1.6%
6 1
 
0.5%
7 3
 
1.6%
8 2
 
1.0%
9 3
 
1.6%
ValueCountFrequency (%)
3293 1
0.5%
3249 1
0.5%
2266 1
0.5%
2238 1
0.5%
2030 1
0.5%
1990 1
0.5%
1460 1
0.5%
1426 1
0.5%
724 1
0.5%
612 1
0.5%

Disrupt Public Service
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)11.6%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean3.1263158
Minimum0
Maximum92
Zeros134
Zeros (%)70.2%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:27.454762image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile18.75
Maximum92
Range92
Interquartile range (IQR)1

Descriptive statistics

Standard deviation11.6535
Coefficient of variation (CV)3.7275506
Kurtosis36.784038
Mean3.1263158
Median Absolute Deviation (MAD)0
Skewness5.7309049
Sum594
Variance135.80407
MonotonicityNot monotonic
2024-02-21T22:03:27.570501image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 134
70.2%
1 20
 
10.5%
2 9
 
4.7%
3 6
 
3.1%
5 2
 
1.0%
21 2
 
1.0%
28 2
 
1.0%
11 1
 
0.5%
90 1
 
0.5%
35 1
 
0.5%
Other values (12) 12
 
6.3%
ValueCountFrequency (%)
0 134
70.2%
1 20
 
10.5%
2 9
 
4.7%
3 6
 
3.1%
4 1
 
0.5%
5 2
 
1.0%
6 1
 
0.5%
7 1
 
0.5%
8 1
 
0.5%
9 1
 
0.5%
ValueCountFrequency (%)
92 1
0.5%
90 1
0.5%
55 1
0.5%
52 1
0.5%
35 1
0.5%
28 2
1.0%
23 1
0.5%
21 2
1.0%
16 1
0.5%
12 1
0.5%

Sale purchase illegal drugs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)5.3%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.71052632
Minimum0
Maximum21
Zeros170
Zeros (%)89.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:27.683922image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile5.55
Maximum21
Range21
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8069066
Coefficient of variation (CV)3.9504611
Kurtosis31.019163
Mean0.71052632
Median Absolute Deviation (MAD)0
Skewness5.2447496
Sum135
Variance7.8787246
MonotonicityNot monotonic
2024-02-21T22:03:27.792895image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 170
89.0%
2 7
 
3.7%
8 3
 
1.6%
6 2
 
1.0%
10 2
 
1.0%
21 2
 
1.0%
5 1
 
0.5%
1 1
 
0.5%
13 1
 
0.5%
4 1
 
0.5%
(Missing) 1
 
0.5%
ValueCountFrequency (%)
0 170
89.0%
1 1
 
0.5%
2 7
 
3.7%
4 1
 
0.5%
5 1
 
0.5%
6 2
 
1.0%
8 3
 
1.6%
10 2
 
1.0%
13 1
 
0.5%
21 2
 
1.0%
ValueCountFrequency (%)
21 2
 
1.0%
13 1
 
0.5%
10 2
 
1.0%
8 3
 
1.6%
6 2
 
1.0%
5 1
 
0.5%
4 1
 
0.5%
2 7
 
3.7%
1 1
 
0.5%
0 170
89.0%

Developing own business
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)16.8%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean11.763158
Minimum0
Maximum210
Zeros123
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:27.917272image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile68.7
Maximum210
Range210
Interquartile range (IQR)3

Descriptive statistics

Standard deviation37.414477
Coefficient of variation (CV)3.180649
Kurtosis16.604792
Mean11.763158
Median Absolute Deviation (MAD)0
Skewness4.1206649
Sum2235
Variance1399.8431
MonotonicityNot monotonic
2024-02-21T22:03:28.042579image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 123
64.4%
1 8
 
4.2%
2 7
 
3.7%
3 6
 
3.1%
7 4
 
2.1%
4 3
 
1.6%
13 3
 
1.6%
17 3
 
1.6%
35 3
 
1.6%
10 3
 
1.6%
Other values (22) 27
 
14.1%
ValueCountFrequency (%)
0 123
64.4%
1 8
 
4.2%
2 7
 
3.7%
3 6
 
3.1%
4 3
 
1.6%
5 2
 
1.0%
6 2
 
1.0%
7 4
 
2.1%
8 2
 
1.0%
9 1
 
0.5%
ValueCountFrequency (%)
210 1
0.5%
203 1
0.5%
198 1
0.5%
181 2
1.0%
163 1
0.5%
156 1
0.5%
146 1
0.5%
76 1
0.5%
75 1
0.5%
61 1
0.5%

Spreading Piracy
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)12.1%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean13.952632
Minimum0
Maximum671
Zeros133
Zeros (%)69.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:28.158033image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile39.85
Maximum671
Range671
Interquartile range (IQR)1

Descriptive statistics

Standard deviation82.097393
Coefficient of variation (CV)5.8840078
Kurtosis57.093907
Mean13.952632
Median Absolute Deviation (MAD)0
Skewness7.5546153
Sum2651
Variance6739.9819
MonotonicityNot monotonic
2024-02-21T22:03:28.279472image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 133
69.6%
2 14
 
7.3%
1 11
 
5.8%
4 5
 
2.6%
3 3
 
1.6%
6 3
 
1.6%
8 2
 
1.0%
75 2
 
1.0%
43 2
 
1.0%
5 2
 
1.0%
Other values (13) 13
 
6.8%
ValueCountFrequency (%)
0 133
69.6%
1 11
 
5.8%
2 14
 
7.3%
3 3
 
1.6%
4 5
 
2.6%
5 2
 
1.0%
6 3
 
1.6%
7 1
 
0.5%
8 2
 
1.0%
12 1
 
0.5%
ValueCountFrequency (%)
671 1
0.5%
669 1
0.5%
614 1
0.5%
90 1
0.5%
88 1
0.5%
75 2
1.0%
45 1
0.5%
43 2
1.0%
36 1
0.5%
20 1
0.5%

Psycho or Pervert
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)3.2%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.34736842
Minimum0
Maximum17
Zeros175
Zeros (%)91.6%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:28.397008image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum17
Range17
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.903872
Coefficient of variation (CV)5.4808436
Kurtosis62.544892
Mean0.34736842
Median Absolute Deviation (MAD)0
Skewness7.6231193
Sum66
Variance3.6247285
MonotonicityNot monotonic
2024-02-21T22:03:28.508224image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 175
91.6%
1 6
 
3.1%
2 3
 
1.6%
4 3
 
1.6%
17 2
 
1.0%
8 1
 
0.5%
(Missing) 1
 
0.5%
ValueCountFrequency (%)
0 175
91.6%
1 6
 
3.1%
2 3
 
1.6%
4 3
 
1.6%
8 1
 
0.5%
17 2
 
1.0%
ValueCountFrequency (%)
17 2
 
1.0%
8 1
 
0.5%
4 3
 
1.6%
2 3
 
1.6%
1 6
 
3.1%
0 175
91.6%

Steal Information
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)6.8%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2.8578947
Minimum0
Maximum93
Zeros158
Zeros (%)82.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:28.620752image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile8.65
Maximum93
Range93
Interquartile range (IQR)0

Descriptive statistics

Standard deviation13.303372
Coefficient of variation (CV)4.6549551
Kurtosis31.88909
Mean2.8578947
Median Absolute Deviation (MAD)0
Skewness5.6192981
Sum543
Variance176.9797
MonotonicityNot monotonic
2024-02-21T22:03:28.730088image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 158
82.7%
1 12
 
6.3%
3 3
 
1.6%
10 2
 
1.0%
4 2
 
1.0%
5 2
 
1.0%
16 2
 
1.0%
2 2
 
1.0%
93 2
 
1.0%
62 2
 
1.0%
Other values (3) 3
 
1.6%
ValueCountFrequency (%)
0 158
82.7%
1 12
 
6.3%
2 2
 
1.0%
3 3
 
1.6%
4 2
 
1.0%
5 2
 
1.0%
7 1
 
0.5%
10 2
 
1.0%
16 2
 
1.0%
49 1
 
0.5%
ValueCountFrequency (%)
93 2
1.0%
82 1
 
0.5%
62 2
1.0%
49 1
 
0.5%
16 2
1.0%
10 2
1.0%
7 1
 
0.5%
5 2
1.0%
4 2
1.0%
3 3
1.6%

Abetment to Suicide
Categorical

IMBALANCE 

Distinct4
Distinct (%)2.1%
Missing1
Missing (%)0.5%
Memory size1.6 KiB
0.0
183 
5.0
 
3
1.0
 
2
2.0
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters570
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 183
95.8%
5.0 3
 
1.6%
1.0 2
 
1.0%
2.0 2
 
1.0%
(Missing) 1
 
0.5%

Length

2024-02-21T22:03:28.846358image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-21T22:03:29.336208image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
ValueCountFrequency (%)
0.0 183
96.3%
5.0 3
 
1.6%
1.0 2
 
1.1%
2.0 2
 
1.1%

Most occurring characters

ValueCountFrequency (%)
0 373
65.4%
. 190
33.3%
5 3
 
0.5%
1 2
 
0.4%
2 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 380
66.7%
Other Punctuation 190
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 373
98.2%
5 3
 
0.8%
1 2
 
0.5%
2 2
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 570
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 373
65.4%
. 190
33.3%
5 3
 
0.5%
1 2
 
0.4%
2 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 373
65.4%
. 190
33.3%
5 3
 
0.5%
1 2
 
0.4%
2 2
 
0.4%

Others
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct88
Distinct (%)46.3%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean397.96842
Minimum0
Maximum8814
Zeros44
Zeros (%)23.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:29.461942image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median12.5
Q3120.5
95-th percentile2784.6
Maximum8814
Range8814
Interquartile range (IQR)119.5

Descriptive statistics

Standard deviation1367.1754
Coefficient of variation (CV)3.4353865
Kurtosis22.474846
Mean397.96842
Median Absolute Deviation (MAD)12.5
Skewness4.6644155
Sum75614
Variance1869168.5
MonotonicityNot monotonic
2024-02-21T22:03:29.601847image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 44
23.0%
1 17
 
8.9%
2 9
 
4.7%
14 7
 
3.7%
11 6
 
3.1%
4 5
 
2.6%
9 3
 
1.6%
42 3
 
1.6%
6 3
 
1.6%
24 3
 
1.6%
Other values (78) 90
47.1%
ValueCountFrequency (%)
0 44
23.0%
1 17
 
8.9%
2 9
 
4.7%
3 3
 
1.6%
4 5
 
2.6%
5 3
 
1.6%
6 3
 
1.6%
8 1
 
0.5%
9 3
 
1.6%
11 6
 
3.1%
ValueCountFrequency (%)
8814 1
0.5%
8688 1
0.5%
7578 1
0.5%
7524 1
0.5%
4956 1
0.5%
4903 1
0.5%
3948 1
0.5%
3756 1
0.5%
3713 1
0.5%
3483 1
0.5%

Total
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct140
Distinct (%)73.7%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean2279.0579
Minimum0
Maximum50035
Zeros9
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-02-21T22:03:29.742872image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q114.25
median131.5
Q3771.5
95-th percentile11272.45
Maximum50035
Range50035
Interquartile range (IQR)757.25

Descriptive statistics

Standard deviation7683.5055
Coefficient of variation (CV)3.3713516
Kurtosis24.652786
Mean2279.0579
Median Absolute Deviation (MAD)128.5
Skewness4.8670378
Sum433021
Variance59036257
MonotonicityNot monotonic
2024-02-21T22:03:29.887801image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9
 
4.7%
7 5
 
2.6%
1 5
 
2.6%
10 4
 
2.1%
2 4
 
2.1%
3 4
 
2.1%
4 4
 
2.1%
30 3
 
1.6%
8 3
 
1.6%
5 3
 
1.6%
Other values (130) 146
76.4%
ValueCountFrequency (%)
0 9
4.7%
1 5
2.6%
2 4
2.1%
3 4
2.1%
4 4
2.1%
5 3
 
1.6%
6 2
 
1.0%
7 5
2.6%
8 3
 
1.6%
9 1
 
0.5%
ValueCountFrequency (%)
50035 1
0.5%
49708 1
0.5%
44546 1
0.5%
44395 1
0.5%
27248 1
0.5%
27004 1
0.5%
21796 1
0.5%
21593 1
0.5%
12020 1
0.5%
11416 1
0.5%

Interactions

2024-02-21T22:03:22.795720image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:03.839306image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:05.460100image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:06.704626image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:08.022775image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:09.309531image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:10.667426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:12.009826image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:13.348444image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:14.620541image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:15.895933image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:17.169363image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:18.892865image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:20.271628image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:21.536919image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:22.887453image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:03.937519image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:05.547906image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:06.797874image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:08.115588image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:09.405248image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:10.760886image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:12.105066image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:13.438218image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:14.712302image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:15.989917image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:17.622156image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:18.991406image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:20.362374image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:21.627502image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:22.967958image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:04.034452image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:05.623119image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:06.880559image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:08.195245image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:09.491139image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:10.842566image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:12.187206image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:13.518257image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:14.791226image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:16.067551image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:17.717417image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:19.075150image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:20.439773image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:21.706485image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:23.056120image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:04.131269image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:05.709358image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:06.967840image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:08.284954image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:09.584478image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:10.934914image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:12.278190image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:13.603864image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:14.877295image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:16.153062image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:17.811396image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:19.167864image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:20.525640image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:21.793325image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:23.139619image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:04.220948image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:05.791426image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:07.053535image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:08.366905image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:09.673232image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:11.022089image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
2024-02-21T22:03:12.363498image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
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2024-02-21T22:03:22.713299image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/

Correlations

2024-02-21T22:03:29.993853image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Abetment to SuicideAngerCausing DisreputeDeveloping own businessDisrupt Public ServiceExtortionFraudOthersPersonal RevengePrankPsycho or PervertSale purchase illegal drugsSexual ExploitationSpreading PiracySteal InformationTotal
Abetment to Suicide1.0000.2750.2580.2960.3370.2450.2400.2420.2900.2120.4820.3080.2490.3760.2370.242
Anger0.2751.0000.7160.6430.6060.7900.7750.7100.8210.7040.3670.3390.8170.5410.4220.820
Causing Disrepute0.2580.7161.0000.6530.5580.6490.6540.6350.6060.6660.3200.2940.7270.5150.3880.711
Developing own business0.2960.6430.6531.0000.5500.6820.6830.6380.6630.6370.4040.4370.6860.6180.4970.700
Disrupt Public Service0.3370.6060.5580.5501.0000.5730.5390.4910.5420.5590.4490.4140.5620.5690.3880.558
Extortion0.2450.7900.6490.6820.5731.0000.8540.7840.7980.6600.3210.3460.8680.5200.4040.894
Fraud0.2400.7750.6540.6830.5390.8541.0000.7630.7940.5930.3440.3980.8410.5240.4660.973
Others0.2420.7100.6350.6380.4910.7840.7631.0000.7300.6120.3050.3220.8300.5610.4280.832
Personal Revenge0.2900.8210.6060.6630.5420.7980.7940.7301.0000.6430.3310.3580.8190.5470.4030.838
Prank0.2120.7040.6660.6370.5590.6600.5930.6120.6431.0000.2750.3140.6680.5380.3920.649
Psycho or Pervert0.4820.3670.3200.4040.4490.3210.3440.3050.3310.2751.0000.3640.3220.3790.4480.341
Sale purchase illegal drugs0.3080.3390.2940.4370.4140.3460.3980.3220.3580.3140.3641.0000.3420.4020.5030.390
Sexual Exploitation0.2490.8170.7270.6860.5620.8680.8410.8300.8190.6680.3220.3421.0000.5660.4070.894
Spreading Piracy0.3760.5410.5150.6180.5690.5200.5240.5610.5470.5380.3790.4020.5661.0000.4230.550
Steal Information0.2370.4220.3880.4970.3880.4040.4660.4280.4030.3920.4480.5030.4070.4231.0000.468
Total0.2420.8200.7110.7000.5580.8940.9730.8320.8380.6490.3410.3900.8940.5500.4681.000

Missing values

2024-02-21T22:03:24.140859image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-21T22:03:24.383971image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-21T22:03:24.620772image/svg+xmlMatplotlib v3.7.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CityPersonal RevengeAngerFraudExtortionCausing DisreputePrankSexual ExploitationDisrupt Public ServiceSale purchase illegal drugsDeveloping own businessSpreading PiracyPsycho or PervertSteal InformationAbetment to SuicideOthersTotal
0Agra5.00.019.00.00.00.00.00.00.00.00.00.00.00.046.070.0
1Allahabad0.00.0222.011.08.00.00.00.00.00.00.00.00.00.00.0241.0
2Amritsar2.00.05.00.00.00.02.00.00.00.00.00.00.00.00.09.0
3Asansol6.01.03.00.00.00.00.00.00.00.00.00.00.00.011.021.0
4Aurangabad5.02.051.00.00.00.021.00.00.00.00.00.00.00.00.082.0
5Bhopal0.00.04.07.02.00.01.01.00.00.00.00.00.00.00.016.0
6Chandigarh City0.00.019.03.00.07.00.00.00.00.00.00.00.00.01.030.0
7Dhanbad2.00.029.00.00.00.00.00.00.00.00.00.00.00.00.031.0
8Durg-Bhilainagar0.00.00.00.010.00.00.00.00.00.00.00.00.00.00.010.0
9Faridabad0.00.09.00.00.00.00.00.00.00.00.00.00.00.08.017.0
CityPersonal RevengeAngerFraudExtortionCausing DisreputePrankSexual ExploitationDisrupt Public ServiceSale purchase illegal drugsDeveloping own businessSpreading PiracyPsycho or PervertSteal InformationAbetment to SuicideOthersTotal
181A & N Islands0.00.00.01.00.00.02.00.00.00.00.00.00.00.02.05.0
182Chandigarh0.00.07.01.00.00.07.00.00.00.00.00.00.00.02.017.0
183D & N Haveli and Daman & Diu0.00.00.00.00.00.03.00.00.00.00.00.00.00.00.03.0
184Delhi2.04.023.015.00.00.020.00.00.00.00.00.00.00.0104.0168.0
185Jammu & Kashmir3.04.033.09.028.02.012.01.00.07.00.00.00.00.014.0120.0
186Ladakh0.00.00.00.00.00.00.01.00.00.00.00.00.00.00.01.0
187Lakshadweep2.00.00.00.00.00.00.00.00.00.00.00.00.00.01.03.0
188Puducherry0.00.04.03.00.00.00.00.00.00.00.00.00.00.03.010.0
189Total UT(s)7.08.067.029.028.02.044.02.00.07.00.00.00.00.0126.0327.0
190Total All India1470.0822.030142.02440.01706.0254.03293.092.021.0210.075.00.062.00.08814.050035.0

Duplicate rows

Most frequently occurring

CityPersonal RevengeAngerFraudExtortionCausing DisreputePrankSexual ExploitationDisrupt Public ServiceSale purchase illegal drugsDeveloping own businessSpreading PiracyPsycho or PervertSteal InformationAbetment to SuicideOthersTotal# duplicates
0Daman & Diu0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.02
1West Bengal28.09.068.025.02.03.039.00.00.00.00.00.00.00.0158.0335.02